Hi! 👋 I'm Lennart, and I started Bioinframed.

Bioinframed is my take on a modern bioinformatics and data engineering partner. At its core, Bioinframed is built around three principles: enable for the long run, interpret with meaning, and pragmatic execution.

1. Enable for the long run

I've spent my career obsessed with one idea: the best discoveries happen when researchers get their hands directly on their own data, through as few middlemen as possible. After building platform after platform around that belief, I distilled everything I've learned into Harmony: a workspace that lets scientists and bioinformaticians—coders and non-coders alike—manage tabular data together, keep a single source of truth, and run exploratory analyses through an interface that makes sense.

2. Interpret with meaning

Of course, there's more nuance to research than what one platform can provide. Custom data analysis, tools, and pipelines still matter enormously. But the role of a bioinformatician here is changing, as AI writes code now.

What hasn't changed, is understanding how biological data should be interpreted, spotting experimental and methodological biases, communicating methods in a cohorent manner, being creative in how you interrogate an experiment, and telling a story through clear, compelling visualizations.

AI is now a thinking partner and execution workhorse, and a good bioinformatician uses it to guide the decisions that matter.

3. Pragmatic execution

Research now more than ever moves at a fast pace. Priorities shift, timelines change, and the question you're answering today differs from the one you started with last month. Part of the job is knowing when a quick & dirty analysis is what's needed to keep momentum, or when the task has earned a production-ready pipeline instead.

For me, the work doesn't end with a table and a handover. One-off analyses lose value the moment they're delivered: the context, the caveats, and the biological knowledge built up along the way all disappear with them. Instead, I focus on long-term collaboration, scaling resources pragmatically to demand, and keeping knowledge where it matters.

Bioinformatics shouldn’t be a black box. It should build researchers’ ability to work with, understand, and reuse their own data. Are you ready to turn that data into discovery? Let's team up!